ArticlePloS one2026
Predicting smartphone battery time-to-empty(TTE) on an open-source PinePhone platform: A modular electro-thermal continuous-time model.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
There is a high degree of variation in the lifetime of Smartphone battery in the real-world experience where the relationship between electrochemical dynamics and workload inside the device and environmental factors are the factors that define the battery life in a Smartphone. In this work we proceed to elaborate a physically interpretable continuous-time electro-thermal model to predict the state of charge (SOC) evolution and time-to-empty (TTE) of smartphone battery, using open-source PinePhone platform as an example. The closed system of second-order Thevenin equivalent circuit polarization branches (usually two RC branches) constitutes the core of the battery model. Major power-consuming subsystems, such as display, processor, radio-frequency communication, and peripheral workloads, are integrated through a modular total power formulation. The aspect of temperature dependence is integrated using an Arrhenius-type internal resistance model, as well as lumped thermal dynamics. These parameters of model are drawn only on publicly available specifications, in addition to open datasets. The results of simulation covering six common usage conditions suggest that the maximum time which the TTE can take is not less than 28.42 hours and when heavily loaded the TTE will take approximately 1.08 hours. The margin of error of the measurement is less than 3 percent compared to official specifications and within 10 percent of published reference studies. The sensitivity analysis defines the CPU utilization and ambient temperature as the factors of significant importance that dictate the quick battery depletion. Finally, hierarchical optimisation strategies involving workload management strategies as well as display/network adjustments push the threshold of heavy usage by stretching it to 2.65 hours. The suggested scheme provides a certain degree of transparency to the smartphone battery endurance prediction and the energy-saving strategy design.
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